Overview

Dataset statistics

Number of variables21
Number of observations100000
Missing cells1752
Missing cells (%)0.1%
Duplicate rows22
Duplicate rows (%)< 0.1%
Total size in memory16.8 MiB
Average record size in memory176.0 B

Variable types

Numeric10
DateTime1
Text6
Categorical4

Alerts

Dataset has 22 (< 0.1%) duplicate rowsDuplicates
price is highly overall correlated with costHigh correlation
cost is highly overall correlated with priceHigh correlation
nal is highly overall correlated with electronHigh correlation
electron is highly overall correlated with nalHigh correlation
Тип города is highly imbalanced (71.4%)Imbalance
Тип улицы is highly imbalanced (69.3%)Imbalance
Тип номера дома is highly imbalanced (70.1%)Imbalance
amount is highly skewed (γ1 = 122.4826947)Skewed
price is highly skewed (γ1 = 201.5919024)Skewed
cost is highly skewed (γ1 = 201.4310729)Skewed
nal is highly skewed (γ1 = 35.09836797)Skewed
electron is highly skewed (γ1 = 213.3822051)Skewed
avans is highly skewed (γ1 = 270.6520885)Skewed
credit is highly skewed (γ1 = 134.3508037)Skewed
vstrechpredst is highly skewed (γ1 = 185.7721841)Skewed
nal has 23409 (23.4%) zerosZeros
electron has 76671 (76.7%) zerosZeros
avans has 99924 (99.9%) zerosZeros
credit has 99961 (> 99.9%) zerosZeros
vstrechpredst has 99976 (> 99.9%) zerosZeros

Reproduction

Analysis started2023-08-21 16:02:25.230223
Analysis finished2023-08-21 16:03:25.290384
Duration1 minute and 0.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

receiptid
Real number (ℝ)

Distinct99660
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70755830
Minimum37061558
Maximum83709978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:25.382584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37061558
5-th percentile60184061
Q164890920
median70702210
Q376693542
95-th percentile81205664
Maximum83709978
Range46648420
Interquartile range (IQR)11802622

Descriptive statistics

Standard deviation6794096.3
Coefficient of variation (CV)0.096021717
Kurtosis-1.1802868
Mean70755830
Median Absolute Deviation (MAD)5901096
Skewness-0.0064540909
Sum7.075583 × 1012
Variance4.6159744 × 1013
MonotonicityNot monotonic
2023-08-21T19:03:25.536072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62692882 3
 
< 0.1%
66248040 3
 
< 0.1%
71130028 3
 
< 0.1%
62405425 3
 
< 0.1%
66899557 3
 
< 0.1%
71267380 2
 
< 0.1%
68888703 2
 
< 0.1%
69133732 2
 
< 0.1%
70688762 2
 
< 0.1%
70022955 2
 
< 0.1%
Other values (99650) 99975
> 99.9%
ValueCountFrequency (%)
37061558 1
< 0.1%
37061956 1
< 0.1%
47521641 1
< 0.1%
47612297 1
< 0.1%
48131467 1
< 0.1%
48362674 1
< 0.1%
48531350 1
< 0.1%
48555146 1
< 0.1%
49009685 1
< 0.1%
49077562 1
< 0.1%
ValueCountFrequency (%)
83709978 1
< 0.1%
83706756 1
< 0.1%
83681569 1
< 0.1%
83680467 1
< 0.1%
83679398 1
< 0.1%
83660199 1
< 0.1%
83657858 1
< 0.1%
83637242 1
< 0.1%
83632874 1
< 0.1%
83632128 1
< 0.1%

kkt_sn
Real number (ℝ)

Distinct9092
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0822448 × 1011
Minimum1.0620555 × 1011
Maximum1.0849 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:25.687176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0620555 × 1011
5-th percentile1.0820052 × 1011
Q11.082028 × 1011
median1.0820582 × 1011
Q31.0820813 × 1011
95-th percentile1.084071 × 1011
Maximum1.0849 × 1011
Range2.2844521 × 109
Interquartile range (IQR)5336120.2

Descriptive statistics

Standard deviation1.1874324 × 108
Coefficient of variation (CV)0.0010971939
Kurtosis172.84681
Mean1.0822448 × 1011
Median Absolute Deviation (MAD)2679861
Skewness-10.476101
Sum1.0822448 × 1016
Variance1.4099958 × 1016
MonotonicityNot monotonic
2023-08-21T19:03:25.845953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.084028694 × 1011578
 
0.6%
1.082022714 × 1011528
 
0.5%
1.084075406 × 1011436
 
0.4%
1.082020448 × 1011406
 
0.4%
1.084024938 × 1011328
 
0.3%
1.084075348 × 1011273
 
0.3%
1.08403534 × 1011272
 
0.3%
1.084075431 × 1011271
 
0.3%
1.084099522 × 1011270
 
0.3%
1.082016051 × 1011255
 
0.3%
Other values (9082) 96383
96.4%
ValueCountFrequency (%)
1.06205548 × 1011172
0.2%
1.067027559 × 101121
 
< 0.1%
1.067035026 × 101148
 
< 0.1%
1.067058852 × 101148
 
< 0.1%
1.082 × 10115
 
< 0.1%
1.082 × 10111
 
< 0.1%
1.082000001 × 10112
 
< 0.1%
1.082000002 × 10112
 
< 0.1%
1.082000003 × 10119
 
< 0.1%
1.082000004 × 10113
 
< 0.1%
ValueCountFrequency (%)
1.084900001 × 10111
 
< 0.1%
1.084099522 × 1011270
0.3%
1.084098702 × 101111
 
< 0.1%
1.084098196 × 1011215
0.2%
1.084098085 × 10115
 
< 0.1%
1.084097983 × 10112
 
< 0.1%
1.084097873 × 10117
 
< 0.1%
1.084097565 × 10115
 
< 0.1%
1.084097508 × 10111
 
< 0.1%
1.08409745 × 10111
 
< 0.1%

d_date
Date

Distinct62
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2018-06-01 00:00:00
Maximum2018-08-01 00:00:00
2023-08-21T19:03:25.997314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:26.157725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

name
Text

Distinct11869
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:26.384586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length128
Median length123
Mean length18.67016
Min length1

Characters and Unicode

Total characters1867016
Distinct characters154
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6658 ?
Unique (%)6.7%

Sample

1st row0001 СТРОИТЕЛЬНЫЕ И ОТДЕЛОЧНЫЕ МАТЕРИАЛЫ
2nd row0001 ТОВАР
3rd row0001 ЭЛЕКТРОЭНЕРГИЯ
4th row0001 ТОВАР
5th row0001 ТОВАР ПО СВОБОДНОЙ ЦЕНЕ
ValueCountFrequency (%)
0001 63550
 
21.5%
товар 29324
 
9.9%
0002 8694
 
2.9%
изделия 6263
 
2.1%
продукты 4707
 
1.6%
и 3149
 
1.1%
услуги 2899
 
1.0%
товары 2876
 
1.0%
0003 2826
 
1.0%
в 2781
 
0.9%
Other values (13237) 168949
57.1%
2023-08-21T19:03:26.806078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 279427
 
15.0%
202111
 
10.8%
О 88496
 
4.7%
А 81265
 
4.4%
1 78943
 
4.2%
Т 68824
 
3.7%
Р 62612
 
3.4%
Е 55941
 
3.0%
И 49092
 
2.6%
В 46473
 
2.5%
Other values (144) 853832
45.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 831786
44.6%
Decimal Number 426178
22.8%
Lowercase Letter 370314
19.8%
Space Separator 202111
 
10.8%
Other Punctuation 26002
 
1.4%
Dash Punctuation 5136
 
0.3%
Close Punctuation 2462
 
0.1%
Open Punctuation 2456
 
0.1%
Other Symbol 307
 
< 0.1%
Math Symbol 210
 
< 0.1%
Other values (3) 54
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
О 88496
 
10.6%
А 81265
 
9.8%
Т 68824
 
8.3%
Р 62612
 
7.5%
Е 55941
 
6.7%
И 49092
 
5.9%
В 46473
 
5.6%
Н 37516
 
4.5%
Л 36987
 
4.4%
С 32371
 
3.9%
Other values (49) 272209
32.7%
Lowercase Letter
ValueCountFrequency (%)
о 44005
 
11.9%
а 34105
 
9.2%
е 28903
 
7.8%
р 26953
 
7.3%
и 21567
 
5.8%
н 19673
 
5.3%
к 18391
 
5.0%
т 17383
 
4.7%
с 17340
 
4.7%
л 16262
 
4.4%
Other values (49) 125732
34.0%
Other Punctuation
ValueCountFrequency (%)
. 8497
32.7%
" 6044
23.2%
, 5831
22.4%
/ 2848
 
11.0%
% 2052
 
7.9%
* 233
 
0.9%
: 132
 
0.5%
\ 120
 
0.5%
? 106
 
0.4%
! 47
 
0.2%
Other values (3) 92
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 279427
65.6%
1 78943
 
18.5%
2 18809
 
4.4%
3 10586
 
2.5%
5 10025
 
2.4%
4 8366
 
2.0%
6 5722
 
1.3%
9 4817
 
1.1%
8 4777
 
1.1%
7 4706
 
1.1%
Math Symbol
ValueCountFrequency (%)
+ 126
60.0%
~ 53
25.2%
= 20
 
9.5%
< 10
 
4.8%
> 1
 
0.5%
Space Separator
ValueCountFrequency (%)
202111
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5136
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2462
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2456
100.0%
Other Symbol
ValueCountFrequency (%)
307
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 24
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 23
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 1184413
63.4%
Common 664916
35.6%
Latin 17687
 
0.9%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
О 88496
 
7.5%
А 81265
 
6.9%
Т 68824
 
5.8%
Р 62612
 
5.3%
Е 55941
 
4.7%
И 49092
 
4.1%
В 46473
 
3.9%
о 44005
 
3.7%
Н 37516
 
3.2%
Л 36987
 
3.1%
Other values (56) 613202
51.8%
Latin
ValueCountFrequency (%)
e 945
 
5.3%
i 786
 
4.4%
o 709
 
4.0%
S 708
 
4.0%
l 692
 
3.9%
a 690
 
3.9%
E 639
 
3.6%
s 624
 
3.5%
R 618
 
3.5%
m 607
 
3.4%
Other values (42) 10669
60.3%
Common
ValueCountFrequency (%)
0 279427
42.0%
202111
30.4%
1 78943
 
11.9%
2 18809
 
2.8%
3 10586
 
1.6%
5 10025
 
1.5%
. 8497
 
1.3%
4 8366
 
1.3%
" 6044
 
0.9%
, 5831
 
0.9%
Other values (26) 36277
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 1184413
63.4%
ASCII 682296
36.5%
Letterlike Symbols 307
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 279427
41.0%
202111
29.6%
1 78943
 
11.6%
2 18809
 
2.8%
3 10586
 
1.6%
5 10025
 
1.5%
. 8497
 
1.2%
4 8366
 
1.2%
" 6044
 
0.9%
, 5831
 
0.9%
Other values (77) 53657
 
7.9%
Cyrillic
ValueCountFrequency (%)
О 88496
 
7.5%
А 81265
 
6.9%
Т 68824
 
5.8%
Р 62612
 
5.3%
Е 55941
 
4.7%
И 49092
 
4.1%
В 46473
 
3.9%
о 44005
 
3.7%
Н 37516
 
3.2%
Л 36987
 
3.1%
Other values (56) 613202
51.8%
Letterlike Symbols
ValueCountFrequency (%)
307
100.0%

amount
Real number (ℝ)

SKEWED 

Distinct1412
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3178273
Minimum0.001
Maximum8355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:26.972276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum8355
Range8354.999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation42.043469
Coefficient of variation (CV)18.139173
Kurtosis20463.272
Mean2.3178273
Median Absolute Deviation (MAD)0
Skewness122.48269
Sum231782.73
Variance1767.6533
MonotonicityNot monotonic
2023-08-21T19:03:27.118460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 91472
91.5%
2 2288
 
2.3%
3 675
 
0.7%
4 365
 
0.4%
5 290
 
0.3%
10 179
 
0.2%
6 138
 
0.1%
0.5 97
 
0.1%
20 76
 
0.1%
30 69
 
0.1%
Other values (1402) 4351
 
4.4%
ValueCountFrequency (%)
0.001 10
< 0.1%
0.003 1
 
< 0.1%
0.007 1
 
< 0.1%
0.01 2
 
< 0.1%
0.012 1
 
< 0.1%
0.02 1
 
< 0.1%
0.021 1
 
< 0.1%
0.023 1
 
< 0.1%
0.026 1
 
< 0.1%
0.028 1
 
< 0.1%
ValueCountFrequency (%)
8355 1
< 0.1%
6000 1
< 0.1%
2935 1
< 0.1%
2371 1
< 0.1%
2285.53 1
< 0.1%
2224 1
< 0.1%
1979 1
< 0.1%
1966 1
< 0.1%
1950 1
< 0.1%
1925.6 1
< 0.1%

unit
Text

Distinct62
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:27.220082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length2
Mean length2.49388
Min length1

Characters and Unicode

Total characters249388
Distinct characters44
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st row--
2nd row--
3rd row--
4th rowштука
5th row--
ValueCountFrequency (%)
80094
80.1%
штука 8925
 
8.9%
шт 4327
 
4.3%
килограмм 2905
 
2.9%
кг 2652
 
2.7%
литр 393
 
0.4%
порц 128
 
0.1%
штук 105
 
0.1%
метр 93
 
0.1%
л 77
 
0.1%
Other values (36) 306
 
0.3%
2023-08-21T19:03:27.466599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 159518
64.0%
к 14655
 
5.9%
т 13883
 
5.6%
ш 13126
 
5.3%
а 11946
 
4.8%
у 9185
 
3.7%
м 5955
 
2.4%
г 5593
 
2.2%
р 3590
 
1.4%
л 3463
 
1.4%
Other values (34) 8474
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 159518
64.0%
Lowercase Letter 88565
35.5%
Other Punctuation 979
 
0.4%
Uppercase Letter 262
 
0.1%
Decimal Number 57
 
< 0.1%
Space Separator 5
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
к 14655
16.5%
т 13883
15.7%
ш 13126
14.8%
а 11946
13.5%
у 9185
10.4%
м 5955
6.7%
г 5593
 
6.3%
р 3590
 
4.1%
л 3463
 
3.9%
и 3310
 
3.7%
Other values (14) 3859
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
Ш 231
88.2%
Л 8
 
3.1%
Ч 8
 
3.1%
У 6
 
2.3%
К 4
 
1.5%
М 2
 
0.8%
Т 2
 
0.8%
В 1
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 31
54.4%
3 11
 
19.3%
5 7
 
12.3%
0 3
 
5.3%
7 3
 
5.3%
1 2
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 633
64.7%
" 333
34.0%
/ 13
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 159518
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 160561
64.4%
Cyrillic 88827
35.6%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
к 14655
16.5%
т 13883
15.6%
ш 13126
14.8%
а 11946
13.4%
у 9185
10.3%
м 5955
6.7%
г 5593
 
6.3%
р 3590
 
4.0%
л 3463
 
3.9%
и 3310
 
3.7%
Other values (22) 4121
 
4.6%
Common
ValueCountFrequency (%)
- 159518
99.4%
. 633
 
0.4%
" 333
 
0.2%
2 31
 
< 0.1%
/ 13
 
< 0.1%
3 11
 
< 0.1%
5 7
 
< 0.1%
5
 
< 0.1%
0 3
 
< 0.1%
7 3
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160561
64.4%
Cyrillic 88827
35.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 159518
99.4%
. 633
 
0.4%
" 333
 
0.2%
2 31
 
< 0.1%
/ 13
 
< 0.1%
3 11
 
< 0.1%
5 7
 
< 0.1%
5
 
< 0.1%
0 3
 
< 0.1%
7 3
 
< 0.1%
Other values (2) 4
 
< 0.1%
Cyrillic
ValueCountFrequency (%)
к 14655
16.5%
т 13883
15.6%
ш 13126
14.8%
а 11946
13.4%
у 9185
10.3%
м 5955
6.7%
г 5593
 
6.3%
р 3590
 
4.0%
л 3463
 
3.9%
и 3310
 
3.7%
Other values (22) 4121
 
4.6%

price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8998
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1415.7003
Minimum0
Maximum13230000
Zeros79
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:27.619365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q150
median128
Q3379
95-th percentile3000
Maximum13230000
Range13230000
Interquartile range (IQR)329

Descriptive statistics

Standard deviation53215.042
Coefficient of variation (CV)37.589199
Kurtosis45560.746
Mean1415.7003
Median Absolute Deviation (MAD)98
Skewness201.5919
Sum1.4157003 × 108
Variance2.8318407 × 109
MonotonicityNot monotonic
2023-08-21T19:03:27.785758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 2119
 
2.1%
30 1908
 
1.9%
50 1530
 
1.5%
20 1490
 
1.5%
150 1408
 
1.4%
200 1257
 
1.3%
60 1114
 
1.1%
40 1098
 
1.1%
90 1094
 
1.1%
85 1070
 
1.1%
Other values (8988) 85912
85.9%
ValueCountFrequency (%)
0 79
0.1%
0.01 7
 
< 0.1%
0.02 1
 
< 0.1%
0.04 5
 
< 0.1%
0.05 4
 
< 0.1%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 2
 
< 0.1%
0.18 1
 
< 0.1%
0.21 2
 
< 0.1%
ValueCountFrequency (%)
13230000 1
< 0.1%
8704899.2 1
< 0.1%
3060000 1
< 0.1%
3000000 1
< 0.1%
1149579.6 1
< 0.1%
1070000 1
< 0.1%
1000000 1
< 0.1%
900000 1
< 0.1%
852550 1
< 0.1%
700000 1
< 0.1%

cost
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11444
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1449.2929
Minimum0
Maximum13230000
Zeros79
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:27.947201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.4
Q154
median135
Q3380.365
95-th percentile3000
Maximum13230000
Range13230000
Interquartile range (IQR)326.365

Descriptive statistics

Standard deviation53229.124
Coefficient of variation (CV)36.72765
Kurtosis45512.042
Mean1449.2929
Median Absolute Deviation (MAD)103
Skewness201.43107
Sum1.4492929 × 108
Variance2.8333396 × 109
MonotonicityNot monotonic
2023-08-21T19:03:28.112375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 2088
 
2.1%
30 1836
 
1.8%
50 1503
 
1.5%
20 1427
 
1.4%
150 1406
 
1.4%
200 1326
 
1.3%
60 1172
 
1.2%
300 1109
 
1.1%
40 1074
 
1.1%
90 1066
 
1.1%
Other values (11434) 85993
86.0%
ValueCountFrequency (%)
0 79
0.1%
0.01 8
 
< 0.1%
0.02 1
 
< 0.1%
0.03 2
 
< 0.1%
0.04 7
 
< 0.1%
0.05 4
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 4
 
< 0.1%
ValueCountFrequency (%)
13230000 1
< 0.1%
8704899.2 1
< 0.1%
3060000 1
< 0.1%
3000000 1
< 0.1%
1149579.6 1
< 0.1%
1070000 1
< 0.1%
1000000 1
< 0.1%
900000 1
< 0.1%
852550 1
< 0.1%
700000 1
< 0.1%

nal
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct10703
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1219.3027
Minimum0
Maximum1153213.6
Zeros23409
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:28.271646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median100
Q3325
95-th percentile2365
Maximum1153213.6
Range1153213.6
Interquartile range (IQR)311

Descriptive statistics

Standard deviation13971.387
Coefficient of variation (CV)11.458506
Kurtosis1797.7944
Mean1219.3027
Median Absolute Deviation (MAD)100
Skewness35.098368
Sum1.2193027 × 108
Variance1.9519966 × 108
MonotonicityNot monotonic
2023-08-21T19:03:28.433937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23409
 
23.4%
100 1529
 
1.5%
30 1262
 
1.3%
50 1085
 
1.1%
150 1019
 
1.0%
200 990
 
1.0%
20 972
 
1.0%
300 842
 
0.8%
60 812
 
0.8%
90 730
 
0.7%
Other values (10693) 67350
67.3%
ValueCountFrequency (%)
0 23409
23.4%
0.01 8
 
< 0.1%
0.02 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 2
 
< 0.1%
0.15 1
 
< 0.1%
0.25 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 1
 
< 0.1%
ValueCountFrequency (%)
1153213.6 1
< 0.1%
1070000 1
< 0.1%
1000000 1
< 0.1%
852550 1
< 0.1%
700000 1
< 0.1%
640601 1
< 0.1%
602382.2 1
< 0.1%
582194.97 1
< 0.1%
578787.8 1
< 0.1%
578735.35 1
< 0.1%

electron
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6757
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean677.11432
Minimum0
Maximum13230000
Zeros76671
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:28.613296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1035
Maximum13230000
Range13230000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation52182.107
Coefficient of variation (CV)77.065431
Kurtosis49281.549
Mean677.11432
Median Absolute Deviation (MAD)0
Skewness213.38221
Sum67711432
Variance2.7229723 × 109
MonotonicityNot monotonic
2023-08-21T19:03:28.783468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76671
76.7%
100 242
 
0.2%
150 223
 
0.2%
200 210
 
0.2%
300 179
 
0.2%
500 168
 
0.2%
120 159
 
0.2%
90 152
 
0.2%
130 142
 
0.1%
400 139
 
0.1%
Other values (6747) 21715
 
21.7%
ValueCountFrequency (%)
0 76671
76.7%
0.21 2
 
< 0.1%
1 33
 
< 0.1%
1.5 1
 
< 0.1%
2 30
 
< 0.1%
2.5 1
 
< 0.1%
3 4
 
< 0.1%
4 5
 
< 0.1%
5 8
 
< 0.1%
5.7 1
 
< 0.1%
ValueCountFrequency (%)
13230000 1
< 0.1%
8704899.2 1
< 0.1%
3060000 1
< 0.1%
3000000 1
< 0.1%
900000 1
< 0.1%
605715 1
< 0.1%
525000 1
< 0.1%
431775.43 1
< 0.1%
400000 1
< 0.1%
365411.25 1
< 0.1%

avans
Real number (ℝ)

SKEWED  ZEROS 

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7710837
Minimum0
Maximum70000
Zeros99924
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:28.943604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum70000
Range70000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation235.12207
Coefficient of variation (CV)132.75605
Kurtosis79091.766
Mean1.7710837
Median Absolute Deviation (MAD)0
Skewness270.65209
Sum177108.37
Variance55282.389
MonotonicityNot monotonic
2023-08-21T19:03:29.110764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 99924
99.9%
10 6
 
< 0.1%
900 4
 
< 0.1%
800 4
 
< 0.1%
3320 3
 
< 0.1%
5000 3
 
< 0.1%
340 2
 
< 0.1%
180 2
 
< 0.1%
510 2
 
< 0.1%
410 2
 
< 0.1%
Other values (46) 48
 
< 0.1%
ValueCountFrequency (%)
0 99924
99.9%
2 1
 
< 0.1%
10 6
 
< 0.1%
100 1
 
< 0.1%
150 1
 
< 0.1%
155 1
 
< 0.1%
180 2
 
< 0.1%
219 1
 
< 0.1%
220 1
 
< 0.1%
236.34 1
 
< 0.1%
ValueCountFrequency (%)
70000 1
 
< 0.1%
20000 1
 
< 0.1%
5000 3
< 0.1%
4000 1
 
< 0.1%
3710 1
 
< 0.1%
3635 1
 
< 0.1%
3320 3
< 0.1%
3200 1
 
< 0.1%
3000 2
< 0.1%
2750 1
 
< 0.1%

credit
Real number (ℝ)

SKEWED  ZEROS 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9035706
Minimum0
Maximum77001
Zeros99961
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:29.261947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum77001
Range77001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation421.05115
Coefficient of variation (CV)107.86308
Kurtosis20090.766
Mean3.9035706
Median Absolute Deviation (MAD)0
Skewness134.3508
Sum390357.06
Variance177284.07
MonotonicityNot monotonic
2023-08-21T19:03:29.412715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 99961
> 99.9%
5 6
 
< 0.1%
100 3
 
< 0.1%
160 2
 
< 0.1%
31940 1
 
< 0.1%
90 1
 
< 0.1%
2000 1
 
< 0.1%
400 1
 
< 0.1%
250 1
 
< 0.1%
15294 1
 
< 0.1%
Other values (22) 22
 
< 0.1%
ValueCountFrequency (%)
0 99961
> 99.9%
5 6
 
< 0.1%
90 1
 
< 0.1%
95 1
 
< 0.1%
100 3
 
< 0.1%
115 1
 
< 0.1%
121 1
 
< 0.1%
135 1
 
< 0.1%
145 1
 
< 0.1%
160 2
 
< 0.1%
ValueCountFrequency (%)
77001 1
< 0.1%
63000 1
< 0.1%
51000 1
< 0.1%
40664 1
< 0.1%
31940 1
< 0.1%
29886 1
< 0.1%
28915 1
< 0.1%
19063 1
< 0.1%
15294 1
< 0.1%
13498 1
< 0.1%

vstrechpredst
Real number (ℝ)

SKEWED  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.969505
Minimum0
Maximum28502
Zeros99976
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-08-21T19:03:29.759906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum28502
Range28502
Interquartile range (IQR)0

Descriptive statistics

Standard deviation115.38246
Coefficient of variation (CV)119.01173
Kurtosis40966.656
Mean0.969505
Median Absolute Deviation (MAD)0
Skewness185.77218
Sum96950.5
Variance13313.113
MonotonicityNot monotonic
2023-08-21T19:03:29.878957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 99976
> 99.9%
5 6
 
< 0.1%
1550 3
 
< 0.1%
4000 2
 
< 0.1%
6000 2
 
< 0.1%
959.5 1
 
< 0.1%
5000 1
 
< 0.1%
10 1
 
< 0.1%
3100 1
 
< 0.1%
2000 1
 
< 0.1%
Other values (6) 6
 
< 0.1%
ValueCountFrequency (%)
0 99976
> 99.9%
5 6
 
< 0.1%
10 1
 
< 0.1%
199 1
 
< 0.1%
959.5 1
 
< 0.1%
1000 1
 
< 0.1%
1550 3
 
< 0.1%
2000 1
 
< 0.1%
3100 1
 
< 0.1%
4000 2
 
< 0.1%
ValueCountFrequency (%)
28502 1
 
< 0.1%
15000 1
 
< 0.1%
10000 1
 
< 0.1%
6500 1
 
< 0.1%
6000 2
< 0.1%
5000 1
 
< 0.1%
4000 2
< 0.1%
3100 1
 
< 0.1%
2000 1
 
< 0.1%
1550 3
< 0.1%
Distinct7
Distinct (%)< 0.1%
Missing219
Missing (%)0.2%
Memory size1.5 MiB
обл
56440 
край
20728 
Респ
12778 
г
6810 
0
 
2780
Other values (2)
 
245

Length

Max length4
Median length3
Mean length3.1412193
Min length1

Characters and Unicode

Total characters313434
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowобл
2nd rowобл
3rd rowРесп
4th rowобл
5th rowкрай

Common Values

ValueCountFrequency (%)
обл 56440
56.4%
край 20728
 
20.7%
Респ 12778
 
12.8%
г 6810
 
6.8%
0 2780
 
2.8%
АО 240
 
0.2%
Аобл 5
 
< 0.1%
(Missing) 219
 
0.2%

Length

2023-08-21T19:03:30.018172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-21T19:03:30.154373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
обл 56440
56.6%
край 20728
 
20.8%
респ 12778
 
12.8%
г 6810
 
6.8%
0 2780
 
2.8%
ао 240
 
0.2%
аобл 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
о 56445
18.0%
б 56445
18.0%
л 56445
18.0%
к 20728
 
6.6%
р 20728
 
6.6%
а 20728
 
6.6%
й 20728
 
6.6%
Р 12778
 
4.1%
е 12778
 
4.1%
с 12778
 
4.1%
Other values (5) 22853
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 297391
94.9%
Uppercase Letter 13263
 
4.2%
Decimal Number 2780
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
о 56445
19.0%
б 56445
19.0%
л 56445
19.0%
к 20728
 
7.0%
р 20728
 
7.0%
а 20728
 
7.0%
й 20728
 
7.0%
е 12778
 
4.3%
с 12778
 
4.3%
п 12778
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
Р 12778
96.3%
А 245
 
1.8%
О 240
 
1.8%
Decimal Number
ValueCountFrequency (%)
0 2780
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 310654
99.1%
Common 2780
 
0.9%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
о 56445
18.2%
б 56445
18.2%
л 56445
18.2%
к 20728
 
6.7%
р 20728
 
6.7%
а 20728
 
6.7%
й 20728
 
6.7%
Р 12778
 
4.1%
е 12778
 
4.1%
с 12778
 
4.1%
Other values (4) 20073
 
6.5%
Common
ValueCountFrequency (%)
0 2780
100.0%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 310654
99.1%
ASCII 2780
 
0.9%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
о 56445
18.2%
б 56445
18.2%
л 56445
18.2%
к 20728
 
6.7%
р 20728
 
6.7%
а 20728
 
6.7%
й 20728
 
6.7%
Р 12778
 
4.1%
е 12778
 
4.1%
с 12778
 
4.1%
Other values (4) 20073
 
6.5%
ASCII
ValueCountFrequency (%)
0 2780
100.0%
Distinct86
Distinct (%)0.1%
Missing219
Missing (%)0.2%
Memory size1.5 MiB
2023-08-21T19:03:30.329471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length19
Mean length10.321825
Min length1

Characters and Unicode

Total characters1029922
Distinct characters56
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowКурганская
2nd rowТверская
3rd rowКабардино-Балкарская
4th rowИркутская
5th rowСтавропольский
ValueCountFrequency (%)
челябинская 7388
 
7.2%
хабаровский 5110
 
5.0%
краснодарский 4635
 
4.5%
свердловская 3656
 
3.6%
тульская 2969
 
2.9%
новосибирская 2781
 
2.7%
0 2780
 
2.7%
пензенская 2551
 
2.5%
санкт-петербург 2376
 
2.3%
московская 2367
 
2.3%
Other values (85) 65835
64.3%
2023-08-21T19:03:30.696742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
а 131299
12.7%
с 102973
 
10.0%
к 93565
 
9.1%
я 73852
 
7.2%
р 72341
 
7.0%
о 71740
 
7.0%
и 49571
 
4.8%
е 40498
 
3.9%
в 39113
 
3.8%
н 36656
 
3.6%
Other values (46) 318314
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 916372
89.0%
Uppercase Letter 102407
 
9.9%
Dash Punctuation 3464
 
0.3%
Decimal Number 2780
 
0.3%
Space Separator 2667
 
0.3%
Open Punctuation 951
 
0.1%
Close Punctuation 951
 
0.1%
Other Punctuation 330
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
а 131299
14.3%
с 102973
11.2%
к 93565
10.2%
я 73852
 
8.1%
р 72341
 
7.9%
о 71740
 
7.8%
и 49571
 
5.4%
е 40498
 
4.4%
в 39113
 
4.3%
н 36656
 
4.0%
Other values (18) 204764
22.3%
Uppercase Letter
ValueCountFrequency (%)
К 19071
18.6%
С 13959
13.6%
Ч 9473
9.3%
П 9143
8.9%
Т 8317
8.1%
Х 5853
 
5.7%
М 5607
 
5.5%
Н 4937
 
4.8%
Б 4063
 
4.0%
В 3967
 
3.9%
Other values (12) 18017
17.6%
Dash Punctuation
ValueCountFrequency (%)
- 3464
100.0%
Decimal Number
ValueCountFrequency (%)
0 2780
100.0%
Space Separator
ValueCountFrequency (%)
2667
100.0%
Open Punctuation
ValueCountFrequency (%)
( 951
100.0%
Close Punctuation
ValueCountFrequency (%)
) 951
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 1018779
98.9%
Common 11143
 
1.1%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
а 131299
12.9%
с 102973
 
10.1%
к 93565
 
9.2%
я 73852
 
7.2%
р 72341
 
7.1%
о 71740
 
7.0%
и 49571
 
4.9%
е 40498
 
4.0%
в 39113
 
3.8%
н 36656
 
3.6%
Other values (40) 307171
30.2%
Common
ValueCountFrequency (%)
- 3464
31.1%
0 2780
24.9%
2667
23.9%
( 951
 
8.5%
) 951
 
8.5%
/ 330
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 1018779
98.9%
ASCII 11143
 
1.1%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
а 131299
12.9%
с 102973
 
10.1%
к 93565
 
9.2%
я 73852
 
7.2%
р 72341
 
7.1%
о 71740
 
7.0%
и 49571
 
4.9%
е 40498
 
4.0%
в 39113
 
3.8%
н 36656
 
3.6%
Other values (40) 307171
30.2%
ASCII
ValueCountFrequency (%)
- 3464
31.1%
0 2780
24.9%
2667
23.9%
( 951
 
8.5%
) 951
 
8.5%
/ 330
 
3.0%

Тип города
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing219
Missing (%)0.2%
Memory size1.5 MiB
г
79369 
0
20322 
пгт
 
65
с/п
 
13
рп
 
10

Length

Max length6
Median length1
Mean length1.0017639
Min length1

Characters and Unicode

Total characters99957
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowг
2nd rowг
3rd rowг
4th rowг
5th rowг

Common Values

ValueCountFrequency (%)
г 79369
79.4%
0 20322
 
20.3%
пгт 65
 
0.1%
с/п 13
 
< 0.1%
рп 10
 
< 0.1%
массив 2
 
< 0.1%
(Missing) 219
 
0.2%

Length

2023-08-21T19:03:30.912520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-21T19:03:31.061266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
г 79369
79.5%
0 20322
 
20.4%
пгт 65
 
0.1%
с/п 13
 
< 0.1%
рп 10
 
< 0.1%
массив 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
г 79434
79.5%
0 20322
 
20.3%
п 88
 
0.1%
т 65
 
0.1%
с 17
 
< 0.1%
/ 13
 
< 0.1%
р 10
 
< 0.1%
м 2
 
< 0.1%
а 2
 
< 0.1%
и 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79622
79.7%
Decimal Number 20322
 
20.3%
Other Punctuation 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
г 79434
99.8%
п 88
 
0.1%
т 65
 
0.1%
с 17
 
< 0.1%
р 10
 
< 0.1%
м 2
 
< 0.1%
а 2
 
< 0.1%
и 2
 
< 0.1%
в 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 20322
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 79622
79.7%
Common 20335
 
20.3%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
г 79434
99.8%
п 88
 
0.1%
т 65
 
0.1%
с 17
 
< 0.1%
р 10
 
< 0.1%
м 2
 
< 0.1%
а 2
 
< 0.1%
и 2
 
< 0.1%
в 2
 
< 0.1%
Common
ValueCountFrequency (%)
0 20322
99.9%
/ 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 79622
79.7%
ASCII 20335
 
20.3%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
г 79434
99.8%
п 88
 
0.1%
т 65
 
0.1%
с 17
 
< 0.1%
р 10
 
< 0.1%
м 2
 
< 0.1%
а 2
 
< 0.1%
и 2
 
< 0.1%
в 2
 
< 0.1%
ASCII
ValueCountFrequency (%)
0 20322
99.9%
/ 13
 
0.1%
Distinct701
Distinct (%)0.7%
Missing219
Missing (%)0.2%
Memory size1.5 MiB
2023-08-21T19:03:31.274617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length6.7247572
Min length1

Characters and Unicode

Total characters671003
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.1%

Sample

1st rowКурган
2nd rowТоржок
3rd rowБаксан
4th rowБодайбо
5th rowСтаврополь
ValueCountFrequency (%)
0 20322
 
19.6%
хабаровск 4186
 
4.0%
челябинск 2483
 
2.4%
новосибирск 2424
 
2.3%
тула 2241
 
2.2%
чита 1688
 
1.6%
екатеринбург 1589
 
1.5%
пермь 1386
 
1.3%
нижний 1380
 
1.3%
новокузнецк 1380
 
1.3%
Other values (716) 64424
62.2%
2023-08-21T19:03:31.633407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
а 59128
 
8.8%
о 56136
 
8.4%
р 47007
 
7.0%
с 45473
 
6.8%
к 45318
 
6.8%
и 37494
 
5.6%
н 35303
 
5.3%
е 31699
 
4.7%
л 27808
 
4.1%
в 23639
 
3.5%
Other values (55) 261998
39.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 558088
83.2%
Uppercase Letter 85717
 
12.8%
Decimal Number 20323
 
3.0%
Space Separator 3722
 
0.6%
Dash Punctuation 3153
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
а 59128
 
10.6%
о 56136
 
10.1%
р 47007
 
8.4%
с 45473
 
8.1%
к 45318
 
8.1%
и 37494
 
6.7%
н 35303
 
6.3%
е 31699
 
5.7%
л 27808
 
5.0%
в 23639
 
4.2%
Other values (22) 149083
26.7%
Uppercase Letter
ValueCountFrequency (%)
К 11849
13.8%
Н 9265
 
10.8%
С 6273
 
7.3%
Ч 6093
 
7.1%
Т 5425
 
6.3%
В 5372
 
6.3%
П 4487
 
5.2%
Х 4363
 
5.1%
О 3987
 
4.7%
Б 3796
 
4.4%
Other values (19) 24807
28.9%
Decimal Number
ValueCountFrequency (%)
0 20322
> 99.9%
2 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
3722
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 643805
95.9%
Common 27198
 
4.1%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
а 59128
 
9.2%
о 56136
 
8.7%
р 47007
 
7.3%
с 45473
 
7.1%
к 45318
 
7.0%
и 37494
 
5.8%
н 35303
 
5.5%
е 31699
 
4.9%
л 27808
 
4.3%
в 23639
 
3.7%
Other values (51) 234800
36.5%
Common
ValueCountFrequency (%)
0 20322
74.7%
3722
 
13.7%
- 3153
 
11.6%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 643805
95.9%
ASCII 27198
 
4.1%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
а 59128
 
9.2%
о 56136
 
8.7%
р 47007
 
7.3%
с 45473
 
7.1%
к 45318
 
7.0%
и 37494
 
5.8%
н 35303
 
5.5%
е 31699
 
4.9%
л 27808
 
4.3%
в 23639
 
3.7%
Other values (51) 234800
36.5%
ASCII
ValueCountFrequency (%)
0 20322
74.7%
3722
 
13.7%
- 3153
 
11.6%
2 1
 
< 0.1%

Тип улицы
Categorical

IMBALANCE 

Distinct26
Distinct (%)< 0.1%
Missing219
Missing (%)0.2%
Memory size1.5 MiB
ул
76000 
пр-кт
9667 
0
 
4672
ш
 
2649
пер
 
1510
Other values (21)
 
5283

Length

Max length7
Median length2
Mean length2.3023522
Min length1

Characters and Unicode

Total characters229731
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowул
2nd rowул
3rd rowпр-кт
4th rowул
5th rowул

Common Values

ValueCountFrequency (%)
ул 76000
76.0%
пр-кт 9667
 
9.7%
0 4672
 
4.7%
ш 2649
 
2.6%
пер 1510
 
1.5%
пл 1497
 
1.5%
мкр 992
 
1.0%
проезд 853
 
0.9%
б-р 739
 
0.7%
км 375
 
0.4%
Other values (16) 827
 
0.8%

Length

2023-08-21T19:03:31.798073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ул 76000
76.2%
пр-кт 9667
 
9.7%
0 4672
 
4.7%
ш 2649
 
2.7%
пер 1510
 
1.5%
пл 1497
 
1.5%
мкр 992
 
1.0%
проезд 853
 
0.9%
б-р 739
 
0.7%
км 375
 
0.4%
Other values (16) 827
 
0.8%

Most occurring characters

ValueCountFrequency (%)
л 77755
33.8%
у 76056
33.1%
р 14155
 
6.2%
п 13680
 
6.0%
к 11486
 
5.0%
- 10476
 
4.6%
т 10372
 
4.5%
0 4672
 
2.0%
ш 2649
 
1.2%
е 2483
 
1.1%
Other values (12) 5947
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 214580
93.4%
Dash Punctuation 10476
 
4.6%
Decimal Number 4672
 
2.0%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
л 77755
36.2%
у 76056
35.4%
р 14155
 
6.6%
п 13680
 
6.4%
к 11486
 
5.4%
т 10372
 
4.8%
ш 2649
 
1.2%
е 2483
 
1.2%
м 1367
 
0.6%
д 887
 
0.4%
Other values (9) 3690
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 10476
100.0%
Decimal Number
ValueCountFrequency (%)
0 4672
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 214580
93.4%
Common 15151
 
6.6%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
л 77755
36.2%
у 76056
35.4%
р 14155
 
6.6%
п 13680
 
6.4%
к 11486
 
5.4%
т 10372
 
4.8%
ш 2649
 
1.2%
е 2483
 
1.2%
м 1367
 
0.6%
д 887
 
0.4%
Other values (9) 3690
 
1.7%
Common
ValueCountFrequency (%)
- 10476
69.1%
0 4672
30.8%
/ 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 214580
93.4%
ASCII 15151
 
6.6%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
л 77755
36.2%
у 76056
35.4%
р 14155
 
6.6%
п 13680
 
6.4%
к 11486
 
5.4%
т 10372
 
4.8%
ш 2649
 
1.2%
е 2483
 
1.2%
м 1367
 
0.6%
д 887
 
0.4%
Other values (9) 3690
 
1.7%
ASCII
ValueCountFrequency (%)
- 10476
69.1%
0 4672
30.8%
/ 3
 
< 0.1%
Distinct2688
Distinct (%)2.7%
Missing219
Missing (%)0.2%
Memory size1.5 MiB
2023-08-21T19:03:32.013430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length30
Mean length9.6897906
Min length1

Characters and Unicode

Total characters966857
Distinct characters80
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique470 ?
Unique (%)0.5%

Sample

1st rowКоли Мяготина
2nd rowЛуначарского
3rd rowЛенина
4th rowКарла Либкнехта
5th rowЛенина
ValueCountFrequency (%)
ленина 5494
 
4.6%
0 4672
 
3.9%
советская 2169
 
1.8%
им 1682
 
1.4%
мира 1597
 
1.3%
карла 1531
 
1.3%
победы 1454
 
1.2%
маркса 1397
 
1.2%
октября 1140
 
1.0%
октябрьская 1069
 
0.9%
Other values (2716) 96664
81.3%
2023-08-21T19:03:32.425858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
а 120693
 
12.5%
о 92737
 
9.6%
е 59998
 
6.2%
н 58006
 
6.0%
к 53750
 
5.6%
р 52138
 
5.4%
и 49990
 
5.2%
с 46892
 
4.8%
в 45781
 
4.7%
я 39826
 
4.1%
Other values (70) 347046
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 818858
84.7%
Uppercase Letter 109037
 
11.3%
Space Separator 19088
 
2.0%
Decimal Number 13183
 
1.4%
Other Punctuation 3575
 
0.4%
Dash Punctuation 3034
 
0.3%
Open Punctuation 41
 
< 0.1%
Close Punctuation 41
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
а 120693
14.7%
о 92737
11.3%
е 59998
 
7.3%
н 58006
 
7.1%
к 53750
 
6.6%
р 52138
 
6.4%
и 49990
 
6.1%
с 46892
 
5.7%
в 45781
 
5.6%
я 39826
 
4.9%
Other values (23) 199047
24.3%
Uppercase Letter
ValueCountFrequency (%)
К 14032
12.9%
П 10590
 
9.7%
М 10259
 
9.4%
С 9912
 
9.1%
Л 9209
 
8.4%
Б 6950
 
6.4%
Г 5644
 
5.2%
В 4813
 
4.4%
О 4615
 
4.2%
Т 3670
 
3.4%
Other values (20) 29343
26.9%
Decimal Number
ValueCountFrequency (%)
0 6562
49.8%
2 1524
 
11.6%
5 1075
 
8.2%
1 915
 
6.9%
4 864
 
6.6%
9 727
 
5.5%
3 639
 
4.8%
6 461
 
3.5%
8 318
 
2.4%
7 98
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 3464
96.9%
' 96
 
2.7%
/ 15
 
0.4%
Space Separator
ValueCountFrequency (%)
19088
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3034
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 927894
96.0%
Common 38962
 
4.0%
Latin 1
 
< 0.1%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
а 120693
 
13.0%
о 92737
 
10.0%
е 59998
 
6.5%
н 58006
 
6.3%
к 53750
 
5.8%
р 52138
 
5.6%
и 49990
 
5.4%
с 46892
 
5.1%
в 45781
 
4.9%
я 39826
 
4.3%
Other values (52) 308083
33.2%
Common
ValueCountFrequency (%)
19088
49.0%
0 6562
 
16.8%
. 3464
 
8.9%
- 3034
 
7.8%
2 1524
 
3.9%
5 1075
 
2.8%
1 915
 
2.3%
4 864
 
2.2%
9 727
 
1.9%
3 639
 
1.6%
Other values (7) 1070
 
2.7%
Latin
ValueCountFrequency (%)
N 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 927894
96.0%
ASCII 38963
 
4.0%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
а 120693
 
13.0%
о 92737
 
10.0%
е 59998
 
6.5%
н 58006
 
6.3%
к 53750
 
5.8%
р 52138
 
5.6%
и 49990
 
5.4%
с 46892
 
5.1%
в 45781
 
4.9%
я 39826
 
4.3%
Other values (52) 308083
33.2%
ASCII
ValueCountFrequency (%)
19088
49.0%
0 6562
 
16.8%
. 3464
 
8.9%
- 3034
 
7.8%
2 1524
 
3.9%
5 1075
 
2.8%
1 915
 
2.3%
4 864
 
2.2%
9 727
 
1.9%
3 639
 
1.6%
Other values (8) 1071
 
2.7%

Тип номера дома
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing219
Missing (%)0.2%
Memory size1.5 MiB
дом
89704 
0
10062 
в/ч
 
15

Length

Max length3
Median length3
Mean length2.7983183
Min length1

Characters and Unicode

Total characters279219
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowдом
2nd rowдом
3rd row0
4th rowдом
5th rowдом

Common Values

ValueCountFrequency (%)
дом 89704
89.7%
0 10062
 
10.1%
в/ч 15
 
< 0.1%
(Missing) 219
 
0.2%

Length

2023-08-21T19:03:32.593958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-21T19:03:32.707343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
дом 89704
89.9%
0 10062
 
10.1%
в/ч 15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
д 89704
32.1%
о 89704
32.1%
м 89704
32.1%
0 10062
 
3.6%
в 15
 
< 0.1%
/ 15
 
< 0.1%
ч 15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 269142
96.4%
Decimal Number 10062
 
3.6%
Other Punctuation 15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
д 89704
33.3%
о 89704
33.3%
м 89704
33.3%
в 15
 
< 0.1%
ч 15
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 10062
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Cyrillic 269142
96.4%
Common 10077
 
3.6%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
д 89704
33.3%
о 89704
33.3%
м 89704
33.3%
в 15
 
< 0.1%
ч 15
 
< 0.1%
Common
ValueCountFrequency (%)
0 10062
99.9%
/ 15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Cyrillic 269142
96.4%
ASCII 10077
 
3.6%

Most frequent character per block

Cyrillic
ValueCountFrequency (%)
д 89704
33.3%
о 89704
33.3%
м 89704
33.3%
в 15
 
< 0.1%
ч 15
 
< 0.1%
ASCII
ValueCountFrequency (%)
0 10062
99.9%
/ 15
 
0.1%
Distinct1071
Distinct (%)1.1%
Missing219
Missing (%)0.2%
Memory size1.5 MiB
2023-08-21T19:03:32.915240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length8.3280885
Min length1

Characters and Unicode

Total characters830985
Distinct characters36
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)0.1%

Sample

1st row"=""47"""
2nd row"=""37"""
3rd row0
4th row"=""59"""
5th row"=""318/2"""
ValueCountFrequency (%)
0 10062
 
10.1%
1 4204
 
4.2%
2 2592
 
2.6%
5 2095
 
2.1%
3 2086
 
2.1%
7 2051
 
2.1%
10 1800
 
1.8%
6 1630
 
1.6%
4 1626
 
1.6%
16 1477
 
1.5%
Other values (1061) 70162
70.3%
2023-08-21T19:03:33.306747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 538314
64.8%
= 89719
 
10.8%
1 41644
 
5.0%
2 26384
 
3.2%
0 19273
 
2.3%
4 16493
 
2.0%
3 16383
 
2.0%
5 14112
 
1.7%
6 13529
 
1.6%
А 12735
 
1.5%
Other values (26) 42399
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 542891
65.3%
Decimal Number 179762
 
21.6%
Math Symbol 89719
 
10.8%
Uppercase Letter 18552
 
2.2%
Dash Punctuation 48
 
< 0.1%
Connector Punctuation 9
 
< 0.1%
Space Separator 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
А 12735
68.6%
Б 3115
 
16.8%
Г 966
 
5.2%
В 923
 
5.0%
Д 384
 
2.1%
Н 203
 
1.1%
Е 57
 
0.3%
М 43
 
0.2%
Ж 32
 
0.2%
С 31
 
0.2%
Other values (9) 63
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 41644
23.2%
2 26384
14.7%
0 19273
10.7%
4 16493
 
9.2%
3 16383
 
9.1%
5 14112
 
7.9%
6 13529
 
7.5%
7 12067
 
6.7%
8 10999
 
6.1%
9 8878
 
4.9%
Other Punctuation
ValueCountFrequency (%)
" 538314
99.2%
/ 4544
 
0.8%
\ 33
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
= 89719
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 812433
97.8%
Cyrillic 18552
 
2.2%

Most frequent character per script

Cyrillic
ValueCountFrequency (%)
А 12735
68.6%
Б 3115
 
16.8%
Г 966
 
5.2%
В 923
 
5.0%
Д 384
 
2.1%
Н 203
 
1.1%
Е 57
 
0.3%
М 43
 
0.2%
Ж 32
 
0.2%
С 31
 
0.2%
Other values (9) 63
 
0.3%
Common
ValueCountFrequency (%)
" 538314
66.3%
= 89719
 
11.0%
1 41644
 
5.1%
2 26384
 
3.2%
0 19273
 
2.4%
4 16493
 
2.0%
3 16383
 
2.0%
5 14112
 
1.7%
6 13529
 
1.7%
7 12067
 
1.5%
Other values (7) 24515
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 812433
97.8%
Cyrillic 18552
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 538314
66.3%
= 89719
 
11.0%
1 41644
 
5.1%
2 26384
 
3.2%
0 19273
 
2.4%
4 16493
 
2.0%
3 16383
 
2.0%
5 14112
 
1.7%
6 13529
 
1.7%
7 12067
 
1.5%
Other values (7) 24515
 
3.0%
Cyrillic
ValueCountFrequency (%)
А 12735
68.6%
Б 3115
 
16.8%
Г 966
 
5.2%
В 923
 
5.0%
Д 384
 
2.1%
Н 203
 
1.1%
Е 57
 
0.3%
М 43
 
0.2%
Ж 32
 
0.2%
С 31
 
0.2%
Other values (9) 63
 
0.3%

Interactions

2023-08-21T19:03:21.828410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:04.373709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:06.037021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:07.589170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:09.534290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:11.539725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:13.690590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:15.660328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:17.658785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:19.649984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:21.975723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:04.479768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:06.148469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:07.743634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:09.697524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:11.692549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:13.847969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:15.822770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:17.813766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:19.809094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:22.170825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:04.632555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:06.300987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:07.919262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:09.911210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:11.892390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:14.052455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:16.030319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:18.016384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:20.020480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:22.374072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:04.776791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:06.452658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:08.112432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:10.090466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:12.095558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:14.255415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:16.234033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:18.228067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:20.229616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:22.587245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:04.934754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:06.614919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:08.323581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:10.293564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:12.280944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:14.465436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:16.448198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:18.439696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:20.447001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:22.788815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:05.096104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:06.776058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:08.530871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:10.503497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:12.489355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:14.652483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:16.660607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:18.650672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:20.662614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:22.999449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:05.262640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:06.940953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:08.742072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:10.723594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:12.877686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:14.860406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:16.850357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:18.865072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:20.881667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:23.209415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:05.432706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:07.103758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:08.951445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:10.939834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:13.088665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:15.069835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:17.067749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:19.061997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:21.095831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:23.425155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:05.600003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:07.260282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:09.158362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:11.153766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:13.303562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:15.282068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:17.281200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:19.270814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:21.283891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:23.617474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:05.912256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:07.438139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:09.372077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:11.383482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:13.519124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:15.500667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:17.497102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:19.493512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-21T19:03:21.493389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-21T19:03:33.431854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
receiptidkkt_snamountpricecostnalelectronavanscreditvstrechpredstТип регионаТип городаТип улицыТип номера дома
receiptid1.0000.0200.000-0.005-0.004-0.0060.0100.002-0.005-0.0090.0210.0160.0240.028
kkt_sn0.0201.000-0.103-0.068-0.093-0.0550.0170.026-0.0160.0090.1790.0560.1560.088
amount0.000-0.1031.000-0.2190.0380.074-0.0590.0090.0080.0140.0870.0000.0890.000
price-0.005-0.068-0.2191.0000.9500.3260.2410.0130.0090.0090.4000.3810.1620.195
cost-0.004-0.0930.0380.9501.0000.3620.2330.0150.0100.0120.3830.3480.1390.152
nal-0.006-0.0550.0740.3260.3621.000-0.725-0.031-0.017-0.0120.2100.2350.1390.000
electron0.0100.017-0.0590.2410.233-0.7251.000-0.011-0.005-0.0010.2880.1640.0000.288
avans0.0020.0260.0090.0130.015-0.031-0.0111.0000.1100.1400.0000.0000.0000.000
credit-0.005-0.0160.0080.0090.010-0.017-0.0050.1101.0000.1960.0070.0000.0000.000
vstrechpredst-0.0090.0090.0140.0090.012-0.012-0.0010.1400.1961.0000.0390.0000.0630.002
Тип региона0.0210.1790.0870.4000.3830.2100.2880.0000.0070.0391.0000.2780.3360.359
Тип города0.0160.0560.0000.3810.3480.2350.1640.0000.0000.0000.2781.0000.1660.127
Тип улицы0.0240.1560.0890.1620.1390.1390.0000.0000.0000.0630.3360.1661.0000.396
Тип номера дома0.0280.0880.0000.1950.1520.0000.2880.0000.0000.0020.3590.1270.3961.000

Missing values

2023-08-21T19:03:23.899978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-21T19:03:24.380081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-21T19:03:24.899922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

receiptidkkt_snd_datenameamountunitpricecostnalelectronavanscreditvstrechpredstТип регионаИмя регионаТип городаИмя городаТип улицыИмя улицыТип номера домаНомер дома
6857952782574341082050538172018-07-230001 СТРОИТЕЛЬНЫЕ И ОТДЕЛОЧНЫЕ МАТЕРИАЛЫ1.0--1947.51947.50.01947.50.00.00.0облКурганскаягКурганулКоли Мяготинадом"=""47"""
9633993764336581082018228812018-07-180001 ТОВАР1.0--25.025.025.00.00.00.00.0облТверскаягТоржокулЛуначарскогодом"=""37"""
2780605816068011082066913002018-07-310001 ЭЛЕКТРОЭНЕРГИЯ1.0--272.0272.0272.00.00.00.00.0РеспКабардино-БалкарскаягБаксанпр-ктЛенина00
8039130709548241082064404692018-07-010001 ТОВАР1.0штука337.0337.0337.00.00.00.00.0облИркутскаягБодайбоулКарла Либкнехтадом"=""59"""
8406613717875761082025032302018-07-050001 ТОВАР ПО СВОБОДНОЙ ЦЕНЕ1.0--120.0120.0120.00.00.00.00.0крайСтавропольскийгСтавропольулЛенинадом"=""318/2"""
2024067705015181082022713992018-06-300001 БАКАЛЕЙНАЯ ПРОДУКЦИЯ1.0--134.0134.00.01090.040.00.00.0РеспТатарстангНижнекамскулБаки Урманчедом"=""2А"""
2936578714847141082034224222018-07-040322 ПЕЛЬМЕНИ "ПЕТУШОК" /0,8/ П/Э (ЗАМ)1.0килограмм125.0125.01034.910.00.00.00.0облОмскаягОмскулТрудадом"=""21"""
9620217732584981082085046722018-07-100001 ТОВАРЫ БАКАЛЕЙНОЙ ГРУППЫ1.0--50.050.050.00.00.00.00.0РеспЧувашская (Чувашия)гНовочебоксарскул10 Пятилеткидом"=""12"""
3005126605761851082022642902018-06-050001 ТОВАР1.0--62.062.062.00.00.00.00.0облКостромская00улКостромскаядом"=""2Б"""
9734165804262741082073055612018-07-290001 Яйцо1.0руб.156.0156.0156.00.00.00.00.0облЛипецкаягЕлецулОвражнаядом"=""39"""
receiptidkkt_snd_datenameamountunitpricecostnalelectronavanscreditvstrechpredstТип регионаИмя регионаТип городаИмя городаТип улицыИмя улицыТип номера домаНомер дома
4930566773111391082010163102018-07-210001 Продукт1.0--1850.01850.01850.00.00.00.00.0РеспСаха /Якутия/гЛенскулНюйскаядом"=""58"""
2032747683649881082022713992018-06-250001 БАКАЛЕЙНАЯ ПРОДУКЦИЯ1.0--81.081.00.0433.410.00.00.0РеспТатарстангНижнекамскулБаки Урманчедом"=""2А"""
5568974677187771082004387822018-06-240003 ПОСТЕЛЬНОЕ БЕЛЬЕ1.0--190.0190.00.0980.00.00.00.0облНовгородскаягБоровичиулКоммунарнаядом"=""51"""
9183434658688301082013452992018-06-090001 ОХЛАЖДЕННОЕ МЯСО1.0килограмм618.0618.00.0618.00.00.00.0гСанкт-Петербург00улНаличнаядом"=""42"""
2226299736219561082060755942018-07-110001 ТОВАР1.0--102.0102.0102.00.00.00.00.0облКемеровскаягНовокузнецкулКутузовадом"=""5"""
4747959629909171082024362682018-06-130002 СОПУТСТВУЮЩИЕ ТОВАРЫ10.0штука2.020.02030.00.00.00.00.0облКемеровскаягНовокузнецкулЛенинадом"=""72/1"""
9807248698813021082062342242018-06-290001 ЭЛЕКТРОЭНЕРГИЯ1.0--1500.01500.01500.00.00.00.00.0РеспДагестангИзбербашулБуйнакскогодом"=""197"""
116464664032251082078651542018-06-210001 ТОВАР НА СУММУ1.0--55.055.00.055.00.00.00.0облОренбургская00улЦентральнаядом"=""3"""
6974799669166791084075405502018-06-220006 ХЛЕБ1.0--18.018.091.00.00.00.00.0облСвердловскаягНижний ТагилулЗахаровадом"=""1А"""
775923622509841082087984082018-06-100001 ТОВАР НА СУММУ1.0--39.039.039.00.00.00.00.0облОренбургскаягОренбургулКонституции СССРдом"=""20"""

Duplicate rows

Most frequently occurring

receiptidkkt_snd_datenameamountunitpricecostnalelectronavanscreditvstrechpredstТип регионаИмя регионаТип городаИмя городаТип улицыИмя улицыТип номера домаНомер дома# duplicates
3626928821082096500272018-06-060001 ТОВАР1.0--250.0250.07526.00.00.00.00.0РеспБашкортостангСтерлитамакулСухановадом"=""4"""3
9662480401082017981012018-06-200004 41.0--100.0100.015995.00.00.00.00.0облВоронежскаягВоронежпроездМонтажныйдом"=""2"""3
0601147151082017827742018-06-040004 ТОВАР 41.0--100.0100.0424.00.00.00.00.0облВоронежскаягВоронежпр-ктМосковскийдом"=""131Б"""2
1611916401084064185422018-06-070101 Самса 130г.1.0шт34.034.0126.00.00.00.00.0облЛипецкаягЛипецкулЗ.Космодемьянскойдом"=""2Б"""2
2614116341082073598372018-06-080008 Хлеб бородинский1.0--35.035.0105.00.00.00.00.0облНовосибирскаягНовосибирскулПетуховадом"=""69"""2
4629800981082056477232018-06-120003 Хлеб на сыворотке 0,3 гр1.0--12.012.0120.00.00.00.00.0облНовосибирскаягНовосибирскулБольшевистскаядом"=""131/2"""2
5633574381082036947892018-06-090008 БИЛЕТ ВЗРОСЛЫЙ1.0--120.0120.0360.00.00.00.00.0РеспЧувашская (Чувашия)гЧебоксарыулКосмонавта Николаева А.Г.дом"=""6"""2
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7652038071084017796712018-06-180136 С-т Крабовый1.0--40.040.00.0360.00.00.00.0000000002
8656452171082053040222018-06-190002 ПЛОВ БОЛЬШОЙ1.0штука140.0140.01480.00.00.00.00.0РеспКрымгСимферопольулПушкинадом"=""46"""2